why python for machine learning?

1.     Python is easy to understand.

To reiterate, Machine Learning is simply recognizing patterns in your data to be able to make improvements and intelligent decisions on its own.

Python is the most suitable programming language for this because it is easy to understand and you can read it for yourself.

Its readability, non-complexity, and ability for fast prototyping make it a popular language among developers and programmers around the world.

 

2.     Python comes with a large number of libraries.

Many of these inbuilt libraries are for Machine Learning and Artificial Intelligence, and can easily be applied out of the box.

·        SciPy:

SciPy contains different modules for optimization, linear algebra, integration, and statistics. It is mostly used for image manipulation and scientific computations.

·        NumPy:

For Machine Learning, NumPy is used for fundamental numerical computations such as linear algebra, Fourier transform, and random number capabilities.

·        Matplotlib:

Matplotlib has a MATLAB-like user interface and is extremely easy to use. It is used for the visualization of patterns in data. It provides various kinds of plots, charts, and graphs for data visualization.

·        Pandas:

Data analysis can be done using Pandas. As mentioned earlier, before training machines, datasets must be prepared. For data extraction and preparation of datasets, Pandas are highly useful.

·        OpenCV:

The purpose of the OpenCV library is to solve computer vision problems. From sorting images and videos to advanced robotic vision techniques, OpenCV is leveraged.

When OpenCV is combined with other libraries, such as NumPy, a highly optimized library for numerical operations with a MATLAB-style syntax, the number of arms in your arsenal increases as every operation that NumPy may do can be combined with OpenCV. This makes it easier to integrate with other NumPy-based libraries, such as SciPy and Matplotlib.

 

 

3.     Python allows easy and powerful implementation.

With other programming languages, coding beginners or students need to familiarize themselves with the language first before being able to use it for ML or AI.

This is not the case with Python. Even if you only have basic knowledge of the Python language, you can already use if for Machine Learning because of the huge amount of libraries, resources, and tools available for you.

Additionally, you will spend less time writing code and debugging errors on Python than on Java or C++.

4.     Friendly syntax and human-level readability

Python is an object-oriented programming language that uses modern scripting and friendly syntax.

Designed with an almost human-level readability, the scripting nature of Python enables coders and programmers to test their hypothesis and run their algorithms very fast.

Python also has a few more advantages as mentioned below:

·        Python has a great library system.

·        It has a low-entry barrier.

·        Python is flexible and versatile.

·        It offers platform independence.

·        It has multiple visualization options.

·        Python is highly popular.